In modern high rise buildings it is the preferred practice to use computer technology to control (at least in part) the dispatching of the cars of the elevator system.
Exemplary, current, computer controlled, dispatcher systems typically include:
several dispatcher algorithms applicable for various operational periods, such as, for example, up-peak, down-peak, noon-time and off-peak periods; and
various traffic predictions to predict, for example
lobby-generated and lobby-oriented traffic for short intervals in terms of passenger boarding and de-boarding counts and car arrivals and departures, and
floor traffic in terms of passengers boarding and de-boarding in the "up" and "down" directions for short intervals and car arrivals and departures in the "up" and "down" directions for short intervals. These predictions are made for up-peak, down-peak and noon-time periods, as well as for other periods.
The traffic is predicted using, for example, data collected for the past several days for various short intervals. This can be termed "historic" prediction and can use, for example, simple moving averages over several days or exponential smoothing in using the "historic" data in making predictions.
The traffic typically is also predicted using data collected on the current day for several short intervals. This can be termed "real-time" prediction and uses, for example, doubles moving averages [see, for example, U.S. Pat. No. 4,846,311 referred to above] or linear exponential smoothing.
The historic and real-time predictions typically are combined to obtain optimal predictions using, for example, linear relationships. The historic and real-time predictions can also use, for further example, simple and multiple regression models and auto-regressive moving average models [for background on these further models, see, for example, Forecasting Methods and Applications, by Makridakis and Wheelwright (John Wiley & Sons, New York, N.Y.), Part 3 ("Regression Methods"), Chapters 5 ("Simple Regression") and 6 ("Multiple Regression") and Part 4 ("AutoRegressive/Moving Average Time-Series Methods"), etc.], as well as other filtering techniques.
Exemplary elevator applications of some of these prediction techniques for elevator systems are noted below:
1. To select optimal sectors for dynamic channeling.
In U.S. Pat. No. 4,846,311 there is an estimation of the future traffic flow levels of various floors, for, for example, each five (5) minute interval for enhanced channeling and enhanced system performance. This estimation can be made using traffic levels measured during the past few time intervals on the given day, namely as "real time" predictors, and, when available, using traffic levels measured during similar time intervals on previous days, namely as "historic" predictors. The estimated traffic is then used to intelligently group floors into sectors, so that each sector ideally has equal traffic volume for each given five (5) minute period or interval. Such intelligently assigned sectoring reduces passenger queues and the waiting times at the lobby by achieving more accurate uniform loading of the cars of the elevator system. The handling capacity of the elevator system is thus significantly increased.
2. To determine the number of people waiting behind the hall call and to dispatch cars so as to give priority to the floors having larger numbers of people predicted to be waiting, as in queue-based dispatching.
In U.S. Pat. No. 4,838,384 the elevators are efficiently dispatched during peak periods by collecting traffic data in the building and predicting passenger traffic levels as functions of time, a few minutes before the occurrence of the specific levels, based on the past several similar days' and the current day's traffic data, and dispatching the cars using a priority scheme based on the number of people waiting behind the hall calls and the past or expected waiting times of the hall calls. This approach thus utilizes methods of lobby oriented or lobby generated traffic data collection at the lobby and upper floors during the "up-peak" period, the "down-peak" period and noon-time for storage in historic and real time data bases, and uses the historic and real time data to predict passenger traffic levels for short time intervals for various periods of the given day.
3. To determine the floors where crowds are accumulating and to give priority service to such floors by assigning more than one car to such crowded floors.
In U.S. Pat. No. 5,022,497 "artificial intelligence" techniques are used to predict the traffic levels and any crowd build up at the various floors, and these predictions are used to better assign one, two or more cars to the "crowd" predicted floors, either parking them there, if they were empty, or, if in active service, more appropriately assigning the car(s) to the hall calls. Part of the strategy of such a system is the accurate prediction or forecasting of traffic dynamics in the form of "crowds" preferably using single exponential smoothing and/or linear exponential smoothing and numerical integration techniques. Thereby the traffic levels at various floors are predicted by collecting the passengers and car stop counts in real time and real time, and using historic (if available) and real time predictions for the traffic levels, with the real time and historic predictions being relatively weighted using relative factoring coefficients whose summation is unity; i.e. a+b=1, in which EQU X=ax.sub.h +bx.sub.r
where "X" is the combined prediction, "x.sub.h " is the historic prediction and "x.sub.r " is the real time prediction and "a" and "b" are multiplying factors.
4. To predict the people waiting behind a hall call and the car load when the car reaches a hall call floor so as to match the car's spare capacity with the number of people waiting behind the hall call; to minimize excessive stopping of heavily loaded cars; to distribute car loads and stops, etc.,--by enhancing the penalties used in a Relative System Response (RSR) algorithm.
See, U.S. Pat. No. 5,024,295 the elevator cars are dispatched using an algorithm with variable bonuses and penalties using "artificial intelligence" techniques based on historic and real time traffic predictions to predict the number of people behind a hall call, the expected boarding and de-boarding rates at en route stops, and the expected car load at the hall call floor, and varying the RSR bonuses and penalties based on this information to distribute car loads and stops more equitably.
5. To provide preferential service for heavy sectors during up-peak channeling by varying the frequency of service with predicted traffic level.
In U.S. application Ser. No. 07/487,344 above, floors are grouped into sectors which are provided with different frequencies of service based on traffic volume (thus varying the time interval between successive assignments of cars for a sector), so that all cars carry a more nearly equal traffic volume. As a result, the queue length and waiting tire at the lobby can be decreased and the handling capacity of the elevator system increased. "Today's" traffic data is used to predict future traffic levels for a quick response to the current day's traffic variations. Additionally, the efficiency and effectiveness of the system is significantly enhanced by the use of linear exponential smoothing in the real time prediction and of single exponential smoothing in the historic prediction, the use combining of both of them with varying multiplication factors to produce optimized traffic predictions, efficiency and effectiveness of the system.
6. To predict the start and end of peak periods, such as up-peak, down-peak and noon time.
In U.S. application Ser. No. 07/487,574 above passenger boarding and de-boarding counts at the lobby and the car arrival and departure counts at the lobby are collected for each short interval each day. Based on such counts for several days the passenger counts and car counts for the next day are predicted. These counts are also predicted in real time using the current day's data. The real time and historic predictions are then combined to get optimal predictions of passenger counts and car counts for each interval. The peak period starting and ending times are based on the times when the predicted passenger boarding counts or de-boarding counts for the next interval reach specified levels, as a first method. In second method, the lobby boarding rate is calculated using the lobby passenger counts and car departure counts. The lobby de-boarding rate is calculated using the lobby passenger de-boarding counts and car arrival counts. In this second method the times when lobby boarding rate or de-boarding rate reach predetermined levels are used as the start or end of the peak periods. For higher reliability, the peak period times predicted using passenger counts and the peak period times predicted using passenger boarding and de-boarding rates are combined, preferably using a linear function, and used as optimal predictions.
7. To vary the door dwell time at each floor based on the predicted number of people de-boarding and boarding cars at that floor.
In U.S. application Ser. No. 07/508,321 referred to above using appropriate "artificial intelligence" (AI) logic involving, for example, real time and historic predictors, the predicted average number of people boarding the car at each hall call stop and the predicted average number of people de-boarding the car at each car call stop is calculated. Then, the needed passenger transfer time based on the predictions are computed as a function of the car's remaining capacity after de-boarding but before boarding, the total predicted passenger transfer counts and the car size (i.e., total capacity), with these factors then related with an appropriate formula to vary the door dwell times.
Although all of the foregoing elevator applications represent substantial advances in the art, they have not yet reached ultimate perfection under all operational circumstances, particularly where the building's traffic needs are varying in a non-cyclical or non-uniformly repeating pattern.
For example, in the prior use of the foregoing prediction methodologies, the prediction algorithms for a particular elevator system are selected by an elevator systems researcher in the laboratory using limited data collected for limited time period(s) at one or a few buildings. The researcher applies a limited set of algorithms, such as simple moving average, exponential smoothing, double moving average, and linear exponential smoothing (note, e.g. U.S. Pat. Nos. 4,838,384 and 4,846,311 referred to above) to historic and real-time data.
He selects for those algorithms the data collection time intervals based on his best judgement, typically in the range of one (1) minute to five (5) minutes.
He then selects a range of values for the prediction coefficients. He conducts experiments with different combinations of prediction models, data and prediction intervals, prediction coefficients, look-back days (for historic predictions) and look-ahead intervals (for real-time predictions).
Using a criterion of minimizing the sum of the square of prediction errors of the intervals of the period over several days or minimizing the sum of absolute prediction errors of various intervals of the period over several days, the researcher then selects what he feels would be the optimal combination of prediction models, data and prediction intervals, prediction coefficient values, look-back days and look-ahead intervals, etc. The set of values are then typically hard-coded in the prediction algorithms, and then these prediction algorithms are used in all types of buildings, even though they may have traffic patterns varying continuously from day-to-day, forever.
Thus, in this prior approach, the prediction models, the data and prediction intervals, the prediction coefficients, the look-back days (for historic predictions) and look-ahead intervals (for real-time predictions) did not vary with the buildings based on the nature of traffic variations in the buildings.
Hence the selected prediction algorithms and parameters would not result in optimal predictions in all buildings under all circumstances and at all times and thus may not be adequately responsive to future variations in traffic under certain conditions.